This is an implementation for PC-Fairness: A Unified Framework for Measuring Causality-based Fairness in NIPS 2019.
- Our implementation is based on Python 3.6.6 in Windows 10 (64-Bit).
- The python distribution Anaconda or Miniconda is highly recommended.
- Since we utilize the environment management tool
conda
, Miniconda is minimal and sufficient.
To re-produce this repository:
- Recover the environment by
conda env create --file environment.yml --name YOUR_ENV_NAME
. - Run
python D1_PC.py
to get Table 2;python D2_PE.py
andpython D2_CE
to get Table 3;python adult_data_CE.py
to get Table 4.
Please cite the original paper if you use this implementation in your manuscript.
@inproceedings{DBLP:conf/nips/Wu0WT19,
author = {Yongkai Wu and
Lu Zhang and
Xintao Wu and
Hanghang Tong},
editor = {Hanna M. Wallach and
Hugo Larochelle and
Alina Beygelzimer and
Florence d'Alch{\'{e}}{-}Buc and
Emily B. Fox and
Roman Garnett},
title = {PC-Fairness: {A} Unified Framework for Measuring Causality-based Fairness},
booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14
December 2019, Vancouver, BC, Canada},
pages = {3399--3409},
year = {2019},
url = {http:https://papers.nips.cc/paper/8601-pc-fairness-a-unified-framework-for-measuring-causality-based-fairness},
timestamp = {Fri, 06 Mar 2020 16:59:11 +0100},
biburl = {https://dblp.org/rec/conf/nips/Wu0WT19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}